There is no admission that the background art disclosed in this section legally constitutes prior art.
Type I diabetes is an autoimmune disease in which the beta-cells of the body are destroyed thus resulting in a lack of insulin production. This leads to an inability to control blood glucose concentration as insulin facilitates the cellular uptake of glucose. If levels of blood glucose concentration remain high for extended periods of time, long-term complications such as neuropathy, nephropathy, vision loss and the like can arise. Due to the lack of insulin production, type I diabetics are required to take insulin subcutaneously as their primary method of therapy.
The major difficulty involving the successful treatment of diabetes is the appropriate dosing of insulin such that a normal physiologic glucose concentration is maintained. There are a multitude of factors which influence subsequent glucose concentrations in diabetics including but not limited to: insulin dosage, carbohydrate and nutritional intake, lifestyle (i.e., sleep-wake cycles and sleep quality, exercise, etc.), and emotional states (i.e., stress, depression, contentment, etc.). The effect of these various factors on subsequent glucose levels is not fully understood, and may be similar across all diabetic patients or patient specific. In order to optimize control in diabetic patients, there needs to be some method for quantifying or predicting future occurrences of dysglycemia (i.e., high and low blood glucose concentration, also referred to as hyperglycemia and hypoglycemia, respectively).
Fluctuations in glucose concentration experienced on an everyday basis appear to be chaotic, however, prior research does elude to possible patterns which may exist. Circadian rhythms in sleep and subsequent glucose regulation have been identified in previous research. Other patterns in insulin activity, insulin sensitivity, and their subsequent effect on glucose concentration have been identified in previous research. The existence of rhythms in insulin activity, and subsequent quantifiable patterns in glucose fluctuations, provide the foundation and construct for the development of the neural network models described herein.
The advent of continuous glucose monitoring (CGM) in the field of diabetes technology provides even more insight for the determination of patterns existent in daily glucose fluctuations of diabetic patients. The usage of CGM technology is also advantageous as it leads to a better understanding of gluco-regulatory dynamics.
Attempts to model blood glucose and insulin interactions in diabetic individuals have been an ongoing topic in current research. The complexity of the neural networks developed in such studies range from simplistic feed-forward neural networks to more complex recurrent networks. In most of these studies, in an attempt to achieve tight glucose control in the normal physiological range, a controller is used to determine the required insulin dosage (based on glucose prediction). The determination of optimal insulin dosages is likely to have considerable error associated with each model as each patient possesses different insulin sensitivities.
In many of the previous endeavors aimed at predicting glucose or optimal insulin dosages to maintain normal glucose concentration, models were generated using inputs including: glucose meter readings, insulin dosages, exercise/activity status, and nutritional intake. While these factors undoubtedly contribute to changes in blood glucose concentration and are quantifiable, there are many factors which are left unrecognized in previous models, particularly other lifestyle and emotional factors.
As mentioned previously, a major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. The overall effect of the factors impacting glucose fluctuations has not been fully quantified to determine the impact on subsequent glycemic trends.
The recent advances in diabetes technology such as real-time continuous glucose monitoring (CGM) provide significant sources of data such that quantification may be possible. Depending on the CGM technology utilized, the sampling frequency ranges from 1-5 minutes.
However, physiological systems and diseases, such as diabetes mellitus which affect such systems, are extremely complex in nature. Attempts to analyze and better understand these types of “systems” have utilized methods such as control engineering. Based on these methods, there have been many attempts aimed at prediction, simulation, and fault detection. Although these methods, in part, provide insight into biological systems, they are still limited due to the inherent complexity of the systems they are attempting to model.
An Artificial Neural Network (ANN) is one approach that is recently gaining considerable interest. In part, this is due to its inherent nature which would seem to be well suited to model complex physiological systems. An ANN functions as a brain within a nervous system, in that it has the ability to distinguish and recognize a particular object from a large set of objects. Neural networks can be utilized to construct a mathematical model of a specific system which is to be controlled.
Another application for the development of such systems which has not received considerable research attention, is in reducing post-traumatic hyperglycemia. Following severe trauma, research indicates that approximately 5% of individuals may experience hyperglycemia. If hyperglycemia is sustained, mortality and requirements for care are potentially increased. Published data indicate that lowering glucose levels after trauma may decrease mortality, the length of stay on ventilators, incidence of infection, and length of stay in the intensive care unit (ICU) and in the hospital. Aggressive therapy to maintain glucose levels below 150 mg % was shown to improve outcomes although the ability to sustain this goal in post-traumatic circumstances may be difficult as the patient recovers.
Continuous glucose monitoring (CGM) in a real-time setting represents a tremendous advantage in such a venue. CGM allows for the assessment of trends in glycemic excursions over an extended period of time. CGM in patients, who have sustained significant trauma, combined with a system capable of anticipating post-traumatic hyperglycemia, may enhance glycemic control and reduce post-trauma glycemic variability, thus potentially reducing infection rates, ventilator days, pneumonia, length of stay in the ICU, and mortality. For example, if glucose levels exceed 200 mg % in several injured patients on admission to trauma centers, their expected survival has been reported to be reduced by more than 50%. Persistence of this hyperglycemia during the first 2 days after trauma has been shown to further reduce survival and increasing glucose levels during this early post-trauma period has been shown to potentially predict adverse outcomes in these patients. Glucose levels greater than 150 mg % during the first 2 post-trauma days is also associated with an increased risk of mortality and/or other complications and subsequent euglycemic maintenance does not appear to improve these outcomes.
Post-traumatic hyperglycemia is a significant health risk and occurs with a relative high frequency. In an unpublished study at the University of Toledo Medical Center, measurements of the initial glucose concentration of 50 Level 1 trauma patients were obtained upon arrival to the critical care unit. Of these, 53% had elevated glucose concentrations (≧150 mg/dL). Of these patients, 34% had glycemic levels within 150-199 mg/dL and were defined as elevated and 19% had glucose concentrations greater than 199 mg/dL and were defined as highly elevated; results of this study are summarized in FIG. 1.
Patients with initial glucose concentrations≧150 mg/dL usually experienced considerable glycemic variability over the course of their stay in the critical care unit.
FIG. 2 illustrates the degree of glycemic variability in a single trauma patient over the course of their stay in the intensive care unit and demonstrates the need for intervention to maintain glucose levels in a normal range.
To minimize the incidence of hyperglycemia following trauma, prompt, aggressive, and sustained treatment is needed, especially to reduce development of adverse outcomes.
Another application for the utilization and development of such predictive systems for glucose include cardio-thoracic surgical patients and other critical care patients which commonly experience elevated glucose. While models for these patients have generated little research attention, the research conducted demonstrates the need for glycemic prediction and optimization of glycemic control in this patient base. For example, patients who undergo some form of cardiovascular surgical intervention are also prone to glycemic fluctuations. Control of glucose concentration in such patients is a desired goal for improving patient outcomes. Also, tight glycemic control in cardiac surgical patients has been correlated to reduced morbidity and mortality rates. Thus, it is integral to patient outcome, that tight glycemic control be obtained in cardiac surgical patients both interoperatively/perioperatively as well as post operatively.
In other venues, such as in a military situation, with current technology, the intervention required is likely to exceed the capability of medics in the field. The ability to make key decisions, such as rapid evacuation or for individuals in remote places where evacuation can be difficult or dangerous, the need for aggressive treatment becomes a critical judgment. There is a need to provide improved monitoring technology and treatment criteria, as well as, rapid and accurate assessment of the appropriate urgency for treatment of the wounded.
In addition, recent research includes:
U.S. Pat. No. 7,052,472: Systems and methods for detecting symptoms of hypoglycemia;
U.S. Pat. No. 7,025,425: Method, system, and computer program product for the evaluation of glycemic control in diabetes from self-monitoring data;
U.S. Pat. No. 6,931,327: System and methods for processing analyte sensor data;
U.S. Pat. No. 6,923,763: Method and apparatus for predicting the risk of hypoglycemia;
U.S. Pat. No. 6,882,940: Methods and devices for prediction of hypoglycemic events:
U.S. Pat. No. 6,658,396: Neural network drug dosage estimation;
U.S. Pat. No. 6,582,366: Medical devices for contemporaneous decision support in metabolic control;
U.S. Pat. No. 6,572,535: Method and apparatus for real-time control of physiological parameters;
U.S. Pat. No. 6,572,542: System and method for monitoring and controlling the glycemic state of a patient;
U.S. Pat. No. 6,544,212: Diabetes management system;
U.S. Pat. No. 6,379,301: Diabetes management system and method for controlling blood glucose;
U.S. Pat. No. 6,272,480: Method and arrangement for the neural modeling of a dynamic system with non-linear stochastic behavior; and
U.S. Pat. No. 7,230,529: System, method, and computer program for interfacing an expert system to a clinical information system.
Therefore, what is needed is an improved supporting algorithm and model for glycemic forecasting and prediction for use with glucose monitoring technologies.
There is a need for improved predictive models for glucose which do not have the prior systems' significant prediction error and limited prediction windows of a few minutes.
It is also desired to have a system that utilizing these glycemic predictions provides the ability to determine insulin dosage estimates for maintaining normal glucose concentration utilizing an algorithm/model which has the capability to learn and adapt given historical trends in glycemic data.
It is further desired to provide a system that has patient/user interaction. The patient and user should be able to select the predictive/forecast window. Such a system should be configured to alert/alarm the user/patient in the event that dysglcyemia (hypoglycemia and hyperglycemia) are predicted, or the system estimates there is a high probability of the occurrence of these unwanted glycemic states.